
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_csv('flood.csv')
print(df)

       MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  \
0                     3                   8                6              6   
1                     8                   4                5              7   
2                     3                  10                4              1   
3                     4                   4                2              7   
4                     3                   7                5              2   
...                 ...                 ...              ...            ...   
49995                 3                   7                4              7   
49996                 3                  10                3              8   
49997                 4                   4                5              7   
49998                 4                   5                4              4   
49999                 4                   5                6              3   

       Urbanization  ClimateChange  DamsQuality  Siltation  \
0                 4              4            6          2   
1                 7              9            1          5   
2                 7              5            4          7   
3                 3              4            1          4   
4                 5              8            5          2   
...             ...            ...          ...        ...   
49995             5              9            4          6   
49996             3              3            4          4   
49997             2              1            4          5   
49998             6              3           10          2   
49999             5              6            5          4   

       AgriculturalPractices  Encroachments  ...  DrainageSystems  \
0                          3              2  ...               10   
1                          5              4  ...                9   
2                          4              9  ...                7   
3                          6              4  ...                4   
4                          7              5  ...                7   
...                      ...            ...  ...              ...   
49995                     10              4  ...                7   
49996                      3             11  ...                8   
49997                      6              7  ...                4   
49998                      6             11  ...                6   
49999                      9             10  ...                2   

       CoastalVulnerability  Landslides  Watersheds  \
0                         7           4           2   
1                         2           6           2   
2                         4           4           8   
3                         2           6           6   
4                         6           5           3   
...                     ...         ...         ...   
49995                     3           8           8   
49996                     6           3           6   
49997                     6           4           1   
49998                     3           4           7   
49999                     4           4           5   

       DeterioratingInfrastructure  PopulationScore  WetlandLoss  \
0                                3                4            3   
1                                1                1            9   
2                                6                1            8   
3                                8                8            6   
4                                3                4            4   
...                            ...              ...          ...   
49995                            6                1            5   
49996                            4                4            2   
49997                            5                1            6   
49998                            6                2            4   
49999                            6                7            8   

       InadequatePlanning  PoliticalFactors  FloodProbability  
0                       2                 6             0.450  
1                       1                 3             0.475  
2                       3                 6             0.515  
3                       6                10             0.520  
4                       3                 4             0.475  
...                   ...               ...               ...  
49995                   4                 2             0.535  
49996                   4                 5             0.510  
49997                   4                 3             0.430  
49998                   0                11             0.515  
49999                  10                 7             0.580  

[50000 rows x 21 columns]


df.head()


df.shape

(50000, 21)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 21 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   MonsoonIntensity                 50000 non-null  int64  
 1   TopographyDrainage               50000 non-null  int64  
 2   RiverManagement                  50000 non-null  int64  
 3   Deforestation                    50000 non-null  int64  
 4   Urbanization                     50000 non-null  int64  
 5   ClimateChange                    50000 non-null  int64  
 6   DamsQuality                      50000 non-null  int64  
 7   Siltation                        50000 non-null  int64  
 8   AgriculturalPractices            50000 non-null  int64  
 9   Encroachments                    50000 non-null  int64  
 10  IneffectiveDisasterPreparedness  50000 non-null  int64  
 11  DrainageSystems                  50000 non-null  int64  
 12  CoastalVulnerability             50000 non-null  int64  
 13  Landslides                       50000 non-null  int64  
 14  Watersheds                       50000 non-null  int64  
 15  DeterioratingInfrastructure      50000 non-null  int64  
 16  PopulationScore                  50000 non-null  int64  
 17  WetlandLoss                      50000 non-null  int64  
 18  InadequatePlanning               50000 non-null  int64  
 19  PoliticalFactors                 50000 non-null  int64  
 20  FloodProbability                 50000 non-null  float64
dtypes: float64(1), int64(20)
memory usage: 8.0 MB


print(df.dtypes)

MonsoonIntensity                     int64
TopographyDrainage                   int64
RiverManagement                      int64
Deforestation                        int64
Urbanization                         int64
ClimateChange                        int64
DamsQuality                          int64
Siltation                            int64
AgriculturalPractices                int64
Encroachments                        int64
IneffectiveDisasterPreparedness      int64
DrainageSystems                      int64
CoastalVulnerability                 int64
Landslides                           int64
Watersheds                           int64
DeterioratingInfrastructure          int64
PopulationScore                      int64
WetlandLoss                          int64
InadequatePlanning                   int64
PoliticalFactors                     int64
FloodProbability                   float64
dtype: object


df.describe()


df.isnull().values.any()

False


df.isnull().values.sum()

0


df.isnull().sum()

MonsoonIntensity                   0
TopographyDrainage                 0
RiverManagement                    0
Deforestation                      0
Urbanization                       0
ClimateChange                      0
DamsQuality                        0
Siltation                          0
AgriculturalPractices              0
Encroachments                      0
IneffectiveDisasterPreparedness    0
DrainageSystems                    0
CoastalVulnerability               0
Landslides                         0
Watersheds                         0
DeterioratingInfrastructure        0
PopulationScore                    0
WetlandLoss                        0
InadequatePlanning                 0
PoliticalFactors                   0
FloodProbability                   0
dtype: int64


print(df.columns)

Index(['MonsoonIntensity', 'TopographyDrainage', 'RiverManagement',
       'Deforestation', 'Urbanization', 'ClimateChange', 'DamsQuality',
       'Siltation', 'AgriculturalPractices', 'Encroachments',
       'IneffectiveDisasterPreparedness', 'DrainageSystems',
       'CoastalVulnerability', 'Landslides', 'Watersheds',
       'DeterioratingInfrastructure', 'PopulationScore', 'WetlandLoss',
       'InadequatePlanning', 'PoliticalFactors', 'FloodProbability'],
      dtype='object')


print(df.tail(n=10))

       MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  \
49990                 5                   8                3              7   
49991                 3                  10                1              3   
49992                 5                   8                7              6   
49993                 4                   4                5              5   
49994                 6                   5                3              5   
49995                 3                   7                4              7   
49996                 3                  10                3              8   
49997                 4                   4                5              7   
49998                 4                   5                4              4   
49999                 4                   5                6              3   

       Urbanization  ClimateChange  DamsQuality  Siltation  \
49990             3              5            2          8   
49991             2              2            6          5   
49992             6              6            4          2   
49993            13              7            2          7   
49994             9              4            6          6   
49995             5              9            4          6   
49996             3              3            4          4   
49997             2              1            4          5   
49998             6              3           10          2   
49999             5              6            5          4   

       AgriculturalPractices  Encroachments  ...  DrainageSystems  \
49990                     12             11  ...                2   
49991                      6              4  ...                6   
49992                      7              6  ...                5   
49993                     10              5  ...                6   
49994                      3              5  ...                6   
49995                     10              4  ...                7   
49996                      3             11  ...                8   
49997                      6              7  ...                4   
49998                      6             11  ...                6   
49999                      9             10  ...                2   

       CoastalVulnerability  Landslides  Watersheds  \
49990                     6           5           4   
49991                     3           3           3   
49992                     1           8           3   
49993                     9           0           7   
49994                     3           6           8   
49995                     3           8           8   
49996                     6           3           6   
49997                     6           4           1   
49998                     3           4           7   
49999                     4           4           5   

       DeterioratingInfrastructure  PopulationScore  WetlandLoss  \
49990                            5                5            6   
49991                            4                2            7   
49992                            6                4            5   
49993                            4                5            6   
49994                            2                9            7   
49995                            6                1            5   
49996                            4                4            2   
49997                            5                1            6   
49998                            6                2            4   
49999                            6                7            8   

       InadequatePlanning  PoliticalFactors  FloodProbability  
49990                   4                 4             0.580  
49991                   5                 4             0.435  
49992                   3                 7             0.520  
49993                   3                 0             0.525  
49994                   5                 4             0.535  
49995                   4                 2             0.535  
49996                   4                 5             0.510  
49997                   4                 3             0.430  
49998                   0                11             0.515  
49999                  10                 7             0.580  

[10 rows x 21 columns]


df.corr

<bound method DataFrame.corr of        MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  \
0                     3                   8                6              6   
1                     8                   4                5              7   
2                     3                  10                4              1   
3                     4                   4                2              7   
4                     3                   7                5              2   
...                 ...                 ...              ...            ...   
49995                 3                   7                4              7   
49996                 3                  10                3              8   
49997                 4                   4                5              7   
49998                 4                   5                4              4   
49999                 4                   5                6              3   

       Urbanization  ClimateChange  DamsQuality  Siltation  \
0                 4              4            6          2   
1                 7              9            1          5   
2                 7              5            4          7   
3                 3              4            1          4   
4                 5              8            5          2   
...             ...            ...          ...        ...   
49995             5              9            4          6   
49996             3              3            4          4   
49997             2              1            4          5   
49998             6              3           10          2   
49999             5              6            5          4   

       AgriculturalPractices  Encroachments  ...  DrainageSystems  \
0                          3              2  ...               10   
1                          5              4  ...                9   
2                          4              9  ...                7   
3                          6              4  ...                4   
4                          7              5  ...                7   
...                      ...            ...  ...              ...   
49995                     10              4  ...                7   
49996                      3             11  ...                8   
49997                      6              7  ...                4   
49998                      6             11  ...                6   
49999                      9             10  ...                2   

       CoastalVulnerability  Landslides  Watersheds  \
0                         7           4           2   
1                         2           6           2   
2                         4           4           8   
3                         2           6           6   
4                         6           5           3   
...                     ...         ...         ...   
49995                     3           8           8   
49996                     6           3           6   
49997                     6           4           1   
49998                     3           4           7   
49999                     4           4           5   

       DeterioratingInfrastructure  PopulationScore  WetlandLoss  \
0                                3                4            3   
1                                1                1            9   
2                                6                1            8   
3                                8                8            6   
4                                3                4            4   
...                            ...              ...          ...   
49995                            6                1            5   
49996                            4                4            2   
49997                            5                1            6   
49998                            6                2            4   
49999                            6                7            8   

       InadequatePlanning  PoliticalFactors  FloodProbability  
0                       2                 6             0.450  
1                       1                 3             0.475  
2                       3                 6             0.515  
3                       6                10             0.520  
4                       3                 4             0.475  
...                   ...               ...               ...  
49995                   4                 2             0.535  
49996                   4                 5             0.510  
49997                   4                 3             0.430  
49998                   0                11             0.515  
49999                  10                 7             0.580  

[50000 rows x 21 columns]>


corr_matrix = df.corr()
# plot the heat map
fig, ax = plt.subplots(figsize=(20, 15))
sns.heatmap(corr_matrix, annot=True, ax=ax, cmap='coolwarm')
plt.show()


from matplotlib import pyplot as grph 
df.hist(figsize=(18, 14), color='blue', edgecolor='black')
grph.tight_layout()
grph.show()


from sklearn.metrics import accuracy_score

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

import matplotlib.pyplot as plt

from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score


from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
# logistic regression 
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()


X=df.copy()

X.drop(columns=['FloodProbability'],inplace=True)

y=df[['FloodProbability']]
print(df)
#splitting the data into training data and test data ( aorund 20% of data will be used for testing the results whereas 80% for training the model)

#X_train contains training data
# y_train cotains result set for training data  ( both X_train and y_train will train the model)
# X_test contains the test data
# y_test contains test expected Results
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

X_train_original=X_train
X_test_original=X_test
y_train_original=y_train
y_test_original=y_test
X_train_original

       MonsoonIntensity  TopographyDrainage  RiverManagement  Deforestation  \
0                     3                   8                6              6   
1                     8                   4                5              7   
2                     3                  10                4              1   
3                     4                   4                2              7   
4                     3                   7                5              2   
...                 ...                 ...              ...            ...   
49995                 3                   7                4              7   
49996                 3                  10                3              8   
49997                 4                   4                5              7   
49998                 4                   5                4              4   
49999                 4                   5                6              3   

       Urbanization  ClimateChange  DamsQuality  Siltation  \
0                 4              4            6          2   
1                 7              9            1          5   
2                 7              5            4          7   
3                 3              4            1          4   
4                 5              8            5          2   
...             ...            ...          ...        ...   
49995             5              9            4          6   
49996             3              3            4          4   
49997             2              1            4          5   
49998             6              3           10          2   
49999             5              6            5          4   

       AgriculturalPractices  Encroachments  ...  DrainageSystems  \
0                          3              2  ...               10   
1                          5              4  ...                9   
2                          4              9  ...                7   
3                          6              4  ...                4   
4                          7              5  ...                7   
...                      ...            ...  ...              ...   
49995                     10              4  ...                7   
49996                      3             11  ...                8   
49997                      6              7  ...                4   
49998                      6             11  ...                6   
49999                      9             10  ...                2   

       CoastalVulnerability  Landslides  Watersheds  \
0                         7           4           2   
1                         2           6           2   
2                         4           4           8   
3                         2           6           6   
4                         6           5           3   
...                     ...         ...         ...   
49995                     3           8           8   
49996                     6           3           6   
49997                     6           4           1   
49998                     3           4           7   
49999                     4           4           5   

       DeterioratingInfrastructure  PopulationScore  WetlandLoss  \
0                                3                4            3   
1                                1                1            9   
2                                6                1            8   
3                                8                8            6   
4                                3                4            4   
...                            ...              ...          ...   
49995                            6                1            5   
49996                            4                4            2   
49997                            5                1            6   
49998                            6                2            4   
49999                            6                7            8   

       InadequatePlanning  PoliticalFactors  FloodProbability  
0                       2                 6             0.450  
1                       1                 3             0.475  
2                       3                 6             0.515  
3                       6                10             0.520  
4                       3                 4             0.475  
...                   ...               ...               ...  
49995                   4                 2             0.535  
49996                   4                 5             0.510  
49997                   4                 3             0.430  
49998                   0                11             0.515  
49999                  10                 7             0.580  

[50000 rows x 21 columns]


selected_attributes = df.select_dtypes(include=['int64', 'float64']).columns.drop('id', errors='ignore')

grph.figure(figsize=(12, 8))
sns.boxplot(data=df[selected_attributes], orient="h")
grph.title('EDA : Boxplot of Selected Attribuites')
grph.xlabel('Magnitude of outcomes')
grph.show()


df.hist(figsize=(20,20),bins=70)
plt.show()


sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x21c360e2550>


df['FloodProbability'].hist()

<Axes: >


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, classification_report

# Assume df is your DataFrame and 'ClimateChange' is your continuous target variable
X = df.drop(columns=['FloodProbability'])  # Exclude the text column from numerical features
y = df['FloodProbability']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; you can adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data for numerical features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression
model = LogisticRegression()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Logistic Regression Validation Accuracy:", np.mean(scores) * 100)

# Decision Tree Classifier
model = DecisionTreeClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Decision Tree Validation Accuracy:", np.mean(scores) * 100)

# Naive Bayes
model = GaussianNB()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Naive Bayes Validation Accuracy:", np.mean(scores) * 100)

# K-Nearest Neighbors
model = KNeighborsClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("K-Nearest Neighbors Validation Accuracy:", np.mean(scores) * 100)

# Random Forest Classifier
model = RandomForestClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Random Forest Validation Accuracy:", np.mean(scores) * 100)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
from sklearn.metrics import mean_squared_error, r2_score

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# Calculate R-squared Score
r2 = r2_score(y_test, y_pred)
print("Linear Regression R-squared Score:", r2)

Logistic Regression Validation Accuracy: 100.0
Decision Tree Validation Accuracy: 69.12
Naive Bayes Validation Accuracy: 91.4625
K-Nearest Neighbors Validation Accuracy: 83.93500000000002
Random Forest Validation Accuracy: 89.42
Linear Regression Mean Squared Error: 0.09023106289377088
Linear Regression R-squared Score: 0.6380971631540853


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, mean_squared_error, r2_score

# Assume df is your DataFrame and 'FloodProbability' is your continuous target variable
X = df.drop(columns=['FloodProbability'])  # Exclude the target variable from features
y = df['FloodProbability']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Logistic Regression Test Accuracy:", accuracy * 100)

# Decision Tree Classifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Decision Tree Test Accuracy:", accuracy * 100)

# Naive Bayes
model = GaussianNB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Naive Bayes Test Accuracy:", accuracy * 100)

# K-Nearest Neighbors
model = KNeighborsClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("K-Nearest Neighbors Test Accuracy:", accuracy * 100)

# Random Forest Classifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Random Forest Test Accuracy:", accuracy * 100)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

Logistic Regression Test Accuracy: 100.0
Decision Tree Test Accuracy: 69.19
Naive Bayes Test Accuracy: 91.18
K-Nearest Neighbors Test Accuracy: 84.11999999999999
Random Forest Test Accuracy: 89.29
Linear Regression Mean Squared Error: 0.09023106289377088
Linear Regression R-squared Score: 0.6380971631540853


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, mean_squared_error, r2_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

# Assume df is your DataFrame and 'FloodProbability' is your continuous target variable
# df = pd.read_csv('your_dataframe.csv') # Uncomment and modify this line to load your DataFrame
X = df.drop(columns=['FloodProbability'])  # Exclude the target variable from features
y = df['FloodProbability']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

def plot_confusion_matrix(y_test, y_pred, title):
    cm = confusion_matrix(y_test, y_pred)
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No Flood', 'Flood'], yticklabels=['No Flood', 'Flood'])
    plt.ylabel('Actual')
    plt.xlabel('Predicted')
    plt.title(title)
    plt.show()

# Logistic Regression
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Logistic Regression Test Accuracy:", accuracy * 100)
plot_confusion_matrix(y_test, y_pred, "Logistic Regression")

# Decision Tree Classifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Decision Tree Test Accuracy:", accuracy * 100)
plot_confusion_matrix(y_test, y_pred, "Decision Tree")

# Naive Bayes
model = GaussianNB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Naive Bayes Test Accuracy:", accuracy * 100)
plot_confusion_matrix(y_test, y_pred, "Naive Bayes")

# K-Nearest Neighbors
model = KNeighborsClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("K-Nearest Neighbors Test Accuracy:", accuracy * 100)
plot_confusion_matrix(y_test, y_pred, "K-Nearest Neighbors")

# Random Forest Classifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Random Forest Test Accuracy:", accuracy * 100)
plot_confusion_matrix(y_test, y_pred, "Random Forest")

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

Logistic Regression Test Accuracy: 100.0

Decision Tree Test Accuracy: 69.42

Naive Bayes Test Accuracy: 91.18

K-Nearest Neighbors Test Accuracy: 84.11999999999999

Random Forest Test Accuracy: 89.05999999999999

Linear Regression Mean Squared Error: 0.09023106289377088
Linear Regression R-squared Score: 0.6380971631540853


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error, r2_score

# Assume df is your DataFrame and 'FloodProbability' is your continuous target variable
# df = ...  # your DataFrame

X = df.drop(columns=['FloodProbability'])  # Exclude the target variable from features
y = df['FloodProbability']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define a function to evaluate the classification model
def evaluate_classification_model(model, X_train, y_train, X_test, y_test):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    print(f"{model.__class__.__name__} Test Accuracy: {accuracy * 100:.2f}%")
    print(f"{model.__class__.__name__} Precision: {precision:.2f}")
    print(f"{model.__class__.__name__} Recall: {recall:.2f}")
    print(f"{model.__class__.__name__} F1-Score: {f1:.2f}")

# Logistic Regression
evaluate_classification_model(LogisticRegression(), X_train, y_train, X_test, y_test)

# Decision Tree Classifier
evaluate_classification_model(DecisionTreeClassifier(), X_train, y_train, X_test, y_test)

# Naive Bayes
evaluate_classification_model(GaussianNB(), X_train, y_train, X_test, y_test)

# K-Nearest Neighbors
evaluate_classification_model(KNeighborsClassifier(), X_train, y_train, X_test, y_test)

# Random Forest Classifier
evaluate_classification_model(RandomForestClassifier(), X_train, y_train, X_test, y_test)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

LogisticRegression Test Accuracy: 100.00%
LogisticRegression Precision: 1.00
LogisticRegression Recall: 1.00
LogisticRegression F1-Score: 1.00
DecisionTreeClassifier Test Accuracy: 69.49%
DecisionTreeClassifier Precision: 0.68
DecisionTreeClassifier Recall: 0.67
DecisionTreeClassifier F1-Score: 0.67
GaussianNB Test Accuracy: 91.18%
GaussianNB Precision: 0.92
GaussianNB Recall: 0.89
GaussianNB F1-Score: 0.91
KNeighborsClassifier Test Accuracy: 84.12%
KNeighborsClassifier Precision: 0.88
KNeighborsClassifier Recall: 0.77
KNeighborsClassifier F1-Score: 0.82
RandomForestClassifier Test Accuracy: 89.41%
RandomForestClassifier Precision: 0.93
RandomForestClassifier Recall: 0.84
RandomForestClassifier F1-Score: 0.88
Linear Regression Mean Squared Error: 0.09023106289377088
Linear Regression R-squared Score: 0.6380971631540853


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, classification_report

# Assume df is your DataFrame and 'ClimateChange' is your continuous target variable
X = df.drop(columns=['ClimateChange'])  # Exclude the text column from numerical features
y = df['ClimateChange']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; you can adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data for numerical features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression
model = LogisticRegression()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Logistic Regression Validation Accuracy:", np.mean(scores) * 100)

# Decision Tree Classifier
model = DecisionTreeClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Decision Tree Validation Accuracy:", np.mean(scores) * 100)

# Naive Bayes
model = GaussianNB()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Naive Bayes Validation Accuracy:", np.mean(scores) * 100)

# K-Nearest Neighbors
model = KNeighborsClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("K-Nearest Neighbors Validation Accuracy:", np.mean(scores) * 100)

# Random Forest Classifier
model = RandomForestClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Random Forest Validation Accuracy:", np.mean(scores) * 100)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
from sklearn.metrics import mean_squared_error, r2_score

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# Calculate R-squared Score
r2 = r2_score(y_test, y_pred)
print("Linear Regression R-squared Score:", r2)

Logistic Regression Validation Accuracy: 99.3425
Decision Tree Validation Accuracy: 98.46999999999998
Naive Bayes Validation Accuracy: 99.3425

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\joblib\externals\loky\backend\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:
[WinError 2] The system cannot find the file specified
Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.
  warnings.warn(
  File "C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\joblib\externals\loky\backend\context.py", line 257, in _count_physical_cores
    cpu_info = subprocess.run(
               ^^^^^^^^^^^^^^^
  File "C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 548, in run
    with Popen(*popenargs, **kwargs) as process:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 1026, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 1538, in _execute_child
    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

K-Nearest Neighbors Validation Accuracy: 99.3425
Random Forest Validation Accuracy: 99.3425
Linear Regression Mean Squared Error: 0.006063152985706354
Linear Regression R-squared Score: 0.03149317115185157


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, classification_report, r2_score

# Assume df is your DataFrame and 'ClimateChange' is your continuous target variable
# df = ...  # your DataFrame

X = df.drop(columns=['ClimateChange'])  # Exclude the target variable from features
y = df['ClimateChange']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; you can adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data for numerical features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Function to print classification report
def print_classification_report(model, X_train, y_train, X_test, y_test):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print(f"Classification Report for {model.__class__.__name__}:\n")
    print(classification_report(y_test, y_pred))

# Logistic Regression
model = LogisticRegression()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Logistic Regression Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Decision Tree Classifier
model = DecisionTreeClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Decision Tree Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Naive Bayes
model = GaussianNB()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Naive Bayes Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# K-Nearest Neighbors
model = KNeighborsClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("K-Nearest Neighbors Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Random Forest Classifier
model = RandomForestClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Random Forest Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

Logistic Regression Validation Accuracy: 99.3425
Classification Report for LogisticRegression:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

Decision Tree Validation Accuracy: 98.42
Classification Report for DecisionTreeClassifier:

              precision    recall  f1-score   support

           0       0.01      0.02      0.01        63
           1       0.99      0.99      0.99      9937

    accuracy                           0.98     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.98      0.99     10000

Naive Bayes Validation Accuracy: 99.3425
Classification Report for GaussianNB:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

K-Nearest Neighbors Validation Accuracy: 99.3425
Classification Report for KNeighborsClassifier:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

Random Forest Validation Accuracy: 99.3425
Classification Report for RandomForestClassifier:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

Linear Regression Mean Squared Error: 0.006063152985706354
Linear Regression R-squared Score: 0.03149317115185157

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, classification_report, confusion_matrix, r2_score

# Assume df is your DataFrame and 'ClimateChange' is your continuous target variable
# df = pd.read_csv('your_dataset.csv') # Uncomment this line and load your dataset
X = df.drop(columns=['ClimateChange'])  # Exclude the text column from numerical features
y = df['ClimateChange']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; you can adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data for numerical features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# List of models to evaluate
models = {
    'Logistic Regression': LogisticRegression(),
    'Decision Tree': DecisionTreeClassifier(),
    'Naive Bayes': GaussianNB(),
    'K-Nearest Neighbors': KNeighborsClassifier(),
    'Random Forest': RandomForestClassifier()
}

# Function to plot confusion matrix
def plot_confusion_matrix(y_true, y_pred, title):
    cm = confusion_matrix(y_true, y_pred)
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)
    plt.title(f'Confusion Matrix for {title}')
    plt.xlabel('Predicted')
    plt.ylabel('Actual')
    plt.show()

# Evaluate models
for name, model in models.items():
    # Perform cross-validation
    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
    print(f"{name} Validation Accuracy: {np.mean(scores) * 100:.2f}%")
    
    # Train and test the model
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    # Plot confusion matrix
    plot_confusion_matrix(y_test, y_pred, name)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

# Plot confusion matrix for Linear Regression (binarized prediction for consistency)
y_pred_binary = (y_pred > threshold).astype(int)
plot_confusion_matrix(y_test, y_pred_binary, "Linear Regression (Binarized)")

Logistic Regression Validation Accuracy: 99.34%

Decision Tree Validation Accuracy: 98.48%

Naive Bayes Validation Accuracy: 99.34%

K-Nearest Neighbors Validation Accuracy: 99.34%

Random Forest Validation Accuracy: 99.34%

Linear Regression Mean Squared Error: 0.006063152985706354
Linear Regression R-squared Score: 0.03149317115185157


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import seaborn as sns

# Sample dataset creation
data = {
    'MonsoonIntensity': np.random.rand(100),
    'TopographyDrainage': np.random.rand(100),
    'RiverManagement': np.random.rand(100),
    'Deforestation': np.random.rand(100),
    'Urbanization': np.random.rand(100),
    'ClimateChange': np.random.rand(100),
    'DamsQuality': np.random.rand(100),
    'Siltation': np.random.rand(100),
    'AgriculturalPractices': np.random.rand(100),
    'Encroachments': np.random.rand(100),
    'IneffectiveDisasterPreparedness': np.random.rand(100),
    'DrainageSystems': np.random.rand(100),
    'CoastalVulnerability': np.random.rand(100),
    'Landslides': np.random.rand(100),
    'Watersheds': np.random.rand(100),
    'DeterioratingInfrastructure': np.random.rand(100),
    'PopulationScore': np.random.rand(100),
    'WetlandLoss': np.random.rand(100),
    'InadequatePlanning': np.random.rand(100),
    'PoliticalFactors': np.random.rand(100),
    'FloodProbability': np.random.rand(100)
}

df = pd.DataFrame(data)

# Splitting data into features and target variable
X = df.drop('FloodProbability', axis=1)
y = df['FloodProbability']

# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Creating and training the model
model = RandomForestRegressor(random_state=42)

# Setting up the parameter grid
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

# Performing grid search
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)

# Getting the best model from grid search
best_model = grid_search.best_estimator_

# Making predictions with the best model
y_best_pred = best_model.predict(X_test)

# Evaluating the best model
best_mse = mean_squared_error(y_test, y_best_pred)
print(f"Best Mean Squared Error: {best_mse}")

# Feature importance
importances = best_model.feature_importances_
feature_importance = pd.DataFrame({'Feature': X.columns, 'Importance': importances})
feature_importance = feature_importance.sort_values(by='Importance', ascending=False)
print(feature_importance)

# Plotting feature importance
plt.figure(figsize=(12, 8))
sns.barplot(x='Importance', y='Feature', data=feature_importance)
plt.title('Feature Importance')
plt.show()

Fitting 5 folds for each of 108 candidates, totalling 540 fits
Best Mean Squared Error: 0.09007535191248463
                            Feature  Importance
7                         Siltation    0.100154
15      DeterioratingInfrastructure    0.079997
4                      Urbanization    0.074658
16                  PopulationScore    0.066043
14                       Watersheds    0.060470
10  IneffectiveDisasterPreparedness    0.059723
17                      WetlandLoss    0.058898
18               InadequatePlanning    0.056367
1                TopographyDrainage    0.051095
9                     Encroachments    0.045737
5                     ClimateChange    0.043783
13                       Landslides    0.042121
6                       DamsQuality    0.042053
2                   RiverManagement    0.041426
19                 PoliticalFactors    0.036528
11                  DrainageSystems    0.033825
0                  MonsoonIntensity    0.030576
3                     Deforestation    0.030089
12             CoastalVulnerability    0.026940
8             AgriculturalPractices    0.019517


import matplotlib.pyplot as plt

# Ensure df is your DataFrame and contains the columns 'MonsoonIntensity', 'TopographyDrainage', 'FloodProbability'
colors = ['red', 'orange', 'blue']
species = df['FloodProbability'].unique()  # Get unique flood probability values for legend and color mapping

# Create a scatter plot for each species
for sp in species:
    # Filter data based on the current species
    x = df[df['FloodProbability'] == sp]
    # Plot the scatter plot with flood probability as color
    plt.scatter(x['MonsoonIntensity'], x['TopographyDrainage'], c=x['FloodProbability'], cmap='viridis', label=sp)

plt.xlabel("Monsoon Intensity")
plt.ylabel("Topography Drainage")
plt.colorbar(label='Flood Probability')  # Add a color bar to indicate flood probability

plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(12,8))
sns.barplot(x='MonsoonIntensity', hue='TopographyDrainage', data=df, ci=None)
plt.title('Bar Plot of MonsoonIntensity by TopographyDrainage')
plt.xlabel('Monsoon Intensity')
plt.ylabel('Count')
plt.legend(title='Topography Drainage')
plt.show()

C:\Users\ibmuser\AppData\Local\Temp\ipykernel_3156\2048272625.py:5: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.barplot(x='MonsoonIntensity', hue='TopographyDrainage', data=df, ci=None)


pip install pydot

Requirement already satisfied: pydot in c:\users\ibmuser\appdata\local\programs\python\python311\lib\site-packages (3.0.1)
Requirement already satisfied: pyparsing>=3.0.9 in c:\users\ibmuser\appdata\local\programs\python\python311\lib\site-packages (from pydot) (3.1.1)
Note: you may need to restart the kernel to use updated packages.


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, classification_report, r2_score

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import plot_model

# Assume df is your DataFrame and 'ClimateChange' is your continuous target variable
# df = ...  # your DataFrame

X = df.drop(columns=['ClimateChange'])  # Exclude the target variable from features
y = df['ClimateChange']

# Binarize the target variable using a threshold
threshold = 0.5  # Example threshold; you can adjust this based on your specific needs
y = (y > threshold).astype(int)

# Split the data for numerical features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Function to print classification report
def print_classification_report(model, X_train, y_train, X_test, y_test):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print(f"Classification Report for {model.__class__.__name__}:\n")
    print(classification_report(y_test, y_pred))

# Logistic Regression
model = LogisticRegression()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Logistic Regression Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Decision Tree Classifier
model = DecisionTreeClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Decision Tree Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Naive Bayes
model = GaussianNB()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Naive Bayes Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# K-Nearest Neighbors
model = KNeighborsClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("K-Nearest Neighbors Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Random Forest Classifier
model = RandomForestClassifier()
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print("Random Forest Validation Accuracy:", np.mean(scores) * 100)
print_classification_report(model, X_train, y_train, X_test, y_test)

# Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Linear Regression Mean Squared Error:", mse)
print("Linear Regression R-squared Score:", r2)

# Neural Network with Keras
nn_model = Sequential()
nn_model.add(Dense(32, input_dim=X_train.shape[1], activation='relu'))
nn_model.add(Dense(16, activation='relu'))
nn_model.add(Dense(1, activation='sigmoid'))

nn_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
history = nn_model.fit(X_train, y_train, epochs=50, batch_size=10, validation_data=(X_test, y_test))

# Evaluate the model
loss, accuracy = nn_model.evaluate(X_test, y_test)
print(f"Neural Network Accuracy: {accuracy * 100}")

# Plot model architecture
plot_model(nn_model, to_file='model.png', show_shapes=True, show_layer_names=True)

# Plot training & validation accuracy values
import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

Logistic Regression Validation Accuracy: 99.3425
Classification Report for LogisticRegression:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

Decision Tree Validation Accuracy: 98.435
Classification Report for DecisionTreeClassifier:

              precision    recall  f1-score   support

           0       0.01      0.02      0.01        63
           1       0.99      0.99      0.99      9937

    accuracy                           0.98     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.98      0.99     10000

Naive Bayes Validation Accuracy: 99.3425
Classification Report for GaussianNB:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

K-Nearest Neighbors Validation Accuracy: 99.3425
Classification Report for KNeighborsClassifier:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

Random Forest Validation Accuracy: 99.3425
Classification Report for RandomForestClassifier:

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        63
           1       0.99      1.00      1.00      9937

    accuracy                           0.99     10000
   macro avg       0.50      0.50      0.50     10000
weighted avg       0.99      0.99      0.99     10000

Linear Regression Mean Squared Error: 0.006063152985706354
Linear Regression R-squared Score: 0.03149317115185157
Epoch 1/50

C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\ibmuser\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

4000/4000 ━━━━━━━━━━━━━━━━━━━━ 5s 998us/step - accuracy: 0.9898 - loss: 0.0514 - val_accuracy: 0.9937 - val_loss: 0.0385
Epoch 2/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 902us/step - accuracy: 0.9935 - loss: 0.0414 - val_accuracy: 0.9937 - val_loss: 0.0406
Epoch 3/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 895us/step - accuracy: 0.9937 - loss: 0.0403 - val_accuracy: 0.9937 - val_loss: 0.0400
Epoch 4/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 882us/step - accuracy: 0.9937 - loss: 0.0399 - val_accuracy: 0.9937 - val_loss: 0.0389
Epoch 5/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 901us/step - accuracy: 0.9925 - loss: 0.0457 - val_accuracy: 0.9937 - val_loss: 0.0392
Epoch 6/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 948us/step - accuracy: 0.9936 - loss: 0.0409 - val_accuracy: 0.9937 - val_loss: 0.0433
Epoch 7/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 878us/step - accuracy: 0.9933 - loss: 0.0423 - val_accuracy: 0.9937 - val_loss: 0.0395
Epoch 8/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 898us/step - accuracy: 0.9939 - loss: 0.0385 - val_accuracy: 0.9937 - val_loss: 0.0385
Epoch 9/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 937us/step - accuracy: 0.9927 - loss: 0.0438 - val_accuracy: 0.9937 - val_loss: 0.0384
Epoch 10/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 919us/step - accuracy: 0.9933 - loss: 0.0411 - val_accuracy: 0.9937 - val_loss: 0.0409
Epoch 11/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 883us/step - accuracy: 0.9933 - loss: 0.0408 - val_accuracy: 0.9937 - val_loss: 0.0420
Epoch 12/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 951us/step - accuracy: 0.9935 - loss: 0.0407 - val_accuracy: 0.9937 - val_loss: 0.0424
Epoch 13/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 956us/step - accuracy: 0.9939 - loss: 0.0376 - val_accuracy: 0.9937 - val_loss: 0.0384
Epoch 14/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 942us/step - accuracy: 0.9930 - loss: 0.0428 - val_accuracy: 0.9937 - val_loss: 0.0383
Epoch 15/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 1ms/step - accuracy: 0.9936 - loss: 0.0394 - val_accuracy: 0.9937 - val_loss: 0.0383
Epoch 16/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 5s 1ms/step - accuracy: 0.9941 - loss: 0.0368 - val_accuracy: 0.9937 - val_loss: 0.0388
Epoch 17/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 1ms/step - accuracy: 0.9931 - loss: 0.0411 - val_accuracy: 0.9937 - val_loss: 0.0397
Epoch 18/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 1ms/step - accuracy: 0.9931 - loss: 0.0412 - val_accuracy: 0.9937 - val_loss: 0.0392
Epoch 19/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 971us/step - accuracy: 0.9930 - loss: 0.0421 - val_accuracy: 0.9937 - val_loss: 0.0389
Epoch 20/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 904us/step - accuracy: 0.9930 - loss: 0.0421 - val_accuracy: 0.9937 - val_loss: 0.0400
Epoch 21/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 904us/step - accuracy: 0.9936 - loss: 0.0391 - val_accuracy: 0.9937 - val_loss: 0.0409
Epoch 22/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 902us/step - accuracy: 0.9935 - loss: 0.0391 - val_accuracy: 0.9937 - val_loss: 0.0390
Epoch 23/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 3s 862us/step - accuracy: 0.9934 - loss: 0.0396 - val_accuracy: 0.9937 - val_loss: 0.0389
Epoch 24/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 962us/step - accuracy: 0.9936 - loss: 0.0382 - val_accuracy: 0.9937 - val_loss: 0.0407
Epoch 25/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 939us/step - accuracy: 0.9935 - loss: 0.0388 - val_accuracy: 0.9937 - val_loss: 0.0404
Epoch 26/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 901us/step - accuracy: 0.9940 - loss: 0.0363 - val_accuracy: 0.9937 - val_loss: 0.0435
Epoch 27/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 984us/step - accuracy: 0.9936 - loss: 0.0387 - val_accuracy: 0.9937 - val_loss: 0.0390
Epoch 28/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 999us/step - accuracy: 0.9926 - loss: 0.0426 - val_accuracy: 0.9937 - val_loss: 0.0387
Epoch 29/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 958us/step - accuracy: 0.9940 - loss: 0.0363 - val_accuracy: 0.9937 - val_loss: 0.0394
Epoch 30/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 972us/step - accuracy: 0.9933 - loss: 0.0393 - val_accuracy: 0.9937 - val_loss: 0.0397
Epoch 31/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 958us/step - accuracy: 0.9937 - loss: 0.0379 - val_accuracy: 0.9937 - val_loss: 0.0407
Epoch 32/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 981us/step - accuracy: 0.9930 - loss: 0.0408 - val_accuracy: 0.9937 - val_loss: 0.0412
Epoch 33/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 984us/step - accuracy: 0.9934 - loss: 0.0389 - val_accuracy: 0.9937 - val_loss: 0.0391
Epoch 34/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 984us/step - accuracy: 0.9932 - loss: 0.0401 - val_accuracy: 0.9937 - val_loss: 0.0412
Epoch 35/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 939us/step - accuracy: 0.9941 - loss: 0.0357 - val_accuracy: 0.9937 - val_loss: 0.0399
Epoch 36/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 954us/step - accuracy: 0.9932 - loss: 0.0394 - val_accuracy: 0.9937 - val_loss: 0.0411
Epoch 37/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 990us/step - accuracy: 0.9932 - loss: 0.0394 - val_accuracy: 0.9937 - val_loss: 0.0391
Epoch 38/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 947us/step - accuracy: 0.9935 - loss: 0.0382 - val_accuracy: 0.9937 - val_loss: 0.0396
Epoch 39/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 913us/step - accuracy: 0.9934 - loss: 0.0382 - val_accuracy: 0.9937 - val_loss: 0.0405
Epoch 40/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 984us/step - accuracy: 0.9936 - loss: 0.0373 - val_accuracy: 0.9937 - val_loss: 0.0404
Epoch 41/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 921us/step - accuracy: 0.9939 - loss: 0.0358 - val_accuracy: 0.9937 - val_loss: 0.0393
Epoch 42/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 975us/step - accuracy: 0.9927 - loss: 0.0415 - val_accuracy: 0.9937 - val_loss: 0.0411
Epoch 43/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 982us/step - accuracy: 0.9932 - loss: 0.0397 - val_accuracy: 0.9937 - val_loss: 0.0437
Epoch 44/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 915us/step - accuracy: 0.9929 - loss: 0.0406 - val_accuracy: 0.9937 - val_loss: 0.0443
Epoch 45/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 982us/step - accuracy: 0.9934 - loss: 0.0387 - val_accuracy: 0.9937 - val_loss: 0.0415
Epoch 46/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 1ms/step - accuracy: 0.9936 - loss: 0.0375 - val_accuracy: 0.9937 - val_loss: 0.0407
Epoch 47/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 977us/step - accuracy: 0.9932 - loss: 0.0390 - val_accuracy: 0.9937 - val_loss: 0.0404
Epoch 48/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 933us/step - accuracy: 0.9928 - loss: 0.0409 - val_accuracy: 0.9937 - val_loss: 0.0461
Epoch 49/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 911us/step - accuracy: 0.9933 - loss: 0.0376 - val_accuracy: 0.9937 - val_loss: 0.0396
Epoch 50/50
4000/4000 ━━━━━━━━━━━━━━━━━━━━ 4s 901us/step - accuracy: 0.9939 - loss: 0.0359 - val_accuracy: 0.9937 - val_loss: 0.0424
313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 685us/step - accuracy: 0.9941 - loss: 0.0391
Neural Network Accuracy: 99.37000274658203

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:1799, in Dot.create(self, prog, format, encoding)
   1798 try:
-> 1799     stdout_data, stderr_data, process = call_graphviz(
   1800         program=prog,
   1801         arguments=arguments,
   1802         working_dir=tmp_dir,
   1803     )
   1804 except OSError as e:

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:222, in call_graphviz(program, arguments, working_dir, **kwargs)
    220 program_with_args = [program] + arguments
--> 222 process = subprocess.Popen(
    223     program_with_args,
    224     env=env,
    225     cwd=working_dir,
    226     shell=False,
    227     stderr=subprocess.PIPE,
    228     stdout=subprocess.PIPE,
    229     **kwargs,
    230 )
    231 stdout_data, stderr_data = process.communicate()

File ~\AppData\Local\Programs\Python\Python311\Lib\subprocess.py:1026, in Popen.__init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize, process_group)
   1023             self.stderr = io.TextIOWrapper(self.stderr,
   1024                     encoding=encoding, errors=errors)
-> 1026     self._execute_child(args, executable, preexec_fn, close_fds,
   1027                         pass_fds, cwd, env,
   1028                         startupinfo, creationflags, shell,
   1029                         p2cread, p2cwrite,
   1030                         c2pread, c2pwrite,
   1031                         errread, errwrite,
   1032                         restore_signals,
   1033                         gid, gids, uid, umask,
   1034                         start_new_session, process_group)
   1035 except:
   1036     # Cleanup if the child failed starting.

File ~\AppData\Local\Programs\Python\Python311\Lib\subprocess.py:1538, in Popen._execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session, unused_process_group)
   1537 try:
-> 1538     hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
   1539                              # no special security
   1540                              None, None,
   1541                              int(not close_fds),
   1542                              creationflags,
   1543                              env,
   1544                              cwd,
   1545                              startupinfo)
   1546 finally:
   1547     # Child is launched. Close the parent's copy of those pipe
   1548     # handles that only the child should have open.  You need
   (...)
   1551     # pipe will not close when the child process exits and the
   1552     # ReadFile will hang.

FileNotFoundError: [WinError 2] The system cannot find the file specified

During handling of the above exception, another exception occurred:

FileNotFoundError                         Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\model_visualization.py:37, in check_graphviz()
     34 try:
     35     # Attempt to create an image of a blank graph
     36     # to check the pydot/graphviz installation.
---> 37     pydot.Dot.create(pydot.Dot())
     38     return True

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pydot\core.py:1808, in Dot.create(self, prog, format, encoding)
   1807     args[1] = f'"{prog}" not found in path.'
-> 1808     raise OSError(*args)
   1809 else:

FileNotFoundError: [WinError 2] "dot" not found in path.

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
Cell In[30], line 91
     88 print(f"Neural Network Accuracy: {accuracy * 100}")
     90 # Plot model architecture
---> 91 plot_model(nn_model, to_file='model.png', show_shapes=True, show_layer_names=True)
     93 # Plot training & validation accuracy values
     94 import matplotlib.pyplot as plt

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\model_visualization.py:430, in plot_model(model, to_file, show_shapes, show_dtype, show_layer_names, rankdir, expand_nested, dpi, show_layer_activations, show_trainable, **kwargs)
    428     else:
    429         raise ImportError(message)
--> 430 if not check_graphviz():
    431     message = (
    432         "You must install graphviz "
    433         "(see instructions at https://graphviz.gitlab.io/download/) "
    434         "for `plot_model` to work."
    435     )
    436     if "IPython.core.magics.namespace" in sys.modules:
    437         # We don't raise an exception here in order to avoid crashing
    438         # notebook tests where graphviz is not available.

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\model_visualization.py:39, in check_graphviz()
     37     pydot.Dot.create(pydot.Dot())
     38     return True
---> 39 except (OSError, pydot.InvocationException):
     40     return False

AttributeError: module 'pydot' has no attribute 'InvocationException'

	MonsoonIntensity	TopographyDrainage	RiverManagement	Deforestation	Urbanization	ClimateChange	DamsQuality	Siltation	AgriculturalPractices	Encroachments	...	DrainageSystems	CoastalVulnerability	Landslides	Watersheds	DeterioratingInfrastructure	PopulationScore	WetlandLoss	InadequatePlanning	PoliticalFactors	FloodProbability
count	50000.000000	50000.000000	50000.00000	50000.000000	50000.000000	50000.000000	50000.00000	50000.000000	50000.000000	50000.000000	...	50000.000000	50000.000000	50000.000000	50000.00000	50000.000000	50000.000000	50000.00000	50000.000000	50000.000000	50000.000000
mean	4.991480	4.984100	5.01594	5.008480	4.989060	4.988340	5.01536	4.988600	5.006120	5.006380	...	5.006060	4.999920	4.984220	4.97982	4.988200	4.984980	5.00512	4.994360	4.990520	0.499660
std	2.236834	2.246488	2.23131	2.222743	2.243159	2.226761	2.24500	2.232642	2.234588	2.241633	...	2.238107	2.247101	2.227741	2.23219	2.231134	2.238279	2.23176	2.230011	2.246075	0.050034
min	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.285000
25%	3.000000	3.000000	3.00000	3.000000	3.000000	3.000000	3.00000	3.000000	3.000000	3.000000	...	3.000000	3.000000	3.000000	3.00000	3.000000	3.000000	3.00000	3.000000	3.000000	0.465000
50%	5.000000	5.000000	5.00000	5.000000	5.000000	5.000000	5.00000	5.000000	5.000000	5.000000	...	5.000000	5.000000	5.000000	5.00000	5.000000	5.000000	5.00000	5.000000	5.000000	0.500000
75%	6.000000	6.000000	6.00000	6.000000	6.000000	6.000000	6.00000	6.000000	6.000000	6.000000	...	6.000000	6.000000	6.000000	6.00000	6.000000	6.000000	6.00000	6.000000	6.000000	0.535000
max	16.000000	18.000000	16.00000	17.000000	17.000000	17.000000	16.00000	16.000000	16.000000	18.000000	...	17.000000	17.000000	16.000000	16.00000	17.000000	19.000000	22.00000	16.000000	16.000000	0.725000

We can come to a conclusion the full CSVfile¶

we can come to conclusion head of csv file¶

We can come to a conclusion that in csv file how many rowsand columns are their in CSVfile¶

we can come to a conclusion that of full information of csv file¶

we can come to a conclusion that in csv file what type of datatype is present¶

We can come to a conclusion that describe of full csv file¶

we can come to a conclusion that in csv file no-null value are present i.e. its false¶

we can come to a conclusion that in csv file no-null value¶

we can come to a conclusion that in csv file no-null value are present¶

We can come to a conclusion that in CSV File that how many column are there¶

We can come to a conclusion that from last it take 10 data from CSV File¶

We can come to a conclusion to show the corelation of each¶

We can come to a conclusion to show the corelation of each by using heat map¶

the boxplot helps you understand the distribution, spread, and presence of outliers in your numerical data, allowing you to identify any attributes that might require further investigation or preprocessing.¶

the histogram provides a comprehensive view of the distribution of all numerical attributes in your dataset, helping you understand their spread, central tendencies, and any potential anomalies or patterns.¶

We can come to a conclusion to all Validation accuracy rate of FloodProbability¶

We can come to a conclusion to all accuracy rate of FloodProbability¶

We can conclude the results for each classifier by analyzing all the matrices.¶

We can come to a conclusion that we can derive the precision, recall, and F1-score of FloodProbability for each algorithm.¶

We can come to a conclusion to all Validation accuracy rate of ClimateChange¶

We can come to a conclusion that we can derive the precision, recall, and F1-score of for ClimateChange of each algorithm¶

We can conclude the results for each classifier by analyzing all the matrices.¶

This code is implementing a RandomForestRegressor model to predict flood probability based on various factors¶

	MonsoonIntensity	TopographyDrainage	RiverManagement	Deforestation	Urbanization	ClimateChange	DamsQuality	Siltation	AgriculturalPractices	Encroachments	...	DrainageSystems	CoastalVulnerability	Landslides	Watersheds	DeterioratingInfrastructure	PopulationScore	WetlandLoss	InadequatePlanning	PoliticalFactors	FloodProbability
0	3	8	6	6	4	4	6	2	3	2	...	10	7	4	2	3	4	3	2	6	0.450
1	8	4	5	7	7	9	1	5	5	4	...	9	2	6	2	1	1	9	1	3	0.475
2	3	10	4	1	7	5	4	7	4	9	...	7	4	4	8	6	1	8	3	6	0.515
3	4	4	2	7	3	4	1	4	6	4	...	4	2	6	6	8	8	6	6	10	0.520
4	3	7	5	2	5	8	5	2	7	5	...	7	6	5	3	3	4	4	3	4	0.475

	MonsoonIntensity	TopographyDrainage	RiverManagement	Deforestation	Urbanization	ClimateChange	DamsQuality	Siltation	AgriculturalPractices	Encroachments	IneffectiveDisasterPreparedness	DrainageSystems	CoastalVulnerability	Landslides	Watersheds	DeterioratingInfrastructure	PopulationScore	WetlandLoss	InadequatePlanning	PoliticalFactors
48893	6	4	3	6	3	7	6	10	8	4	4	3	3	5	1	6	7	3	0	2
1210	7	6	5	3	8	5	4	7	4	1	11	3	7	5	7	2	4	3	5	10
28868	2	5	7	5	4	5	5	6	6	8	4	4	6	3	6	2	8	6	5	4
3618	6	5	1	6	3	3	9	6	6	4	12	4	6	4	6	5	5	3	4	8
24579	7	3	4	3	3	4	5	5	3	4	8	4	2	7	4	7	6	1	3	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10160	9	8	9	2	6	4	9	6	4	6	3	6	3	2	4	9	6	6	8	5
8246	4	4	1	5	5	5	4	7	2	6	6	4	6	5	6	2	3	11	10	3
26176	6	3	8	7	5	1	9	7	10	2	8	3	4	4	5	4	1	2	4	5
4447	4	4	3	7	5	6	7	6	2	2	8	5	8	8	7	6	2	3	3	5
3957	1	4	5	2	6	3	10	6	2	2	5	4	4	6	5	5	3	2	5	2